Analysis Report — travel

Generated on 2025-10-06T23:10:50.122830
Rows: 8000
Positive: 34.3% • Negative: 40.6% • Neutral: 25.1

Executive summary

Overall sentiment: mean VADER compound = 0.08 (std 0.38).
Top positive terms: need, wonderful, convenient, class, paris, staff
Top negative terms: need, overbooked, paris, terrible, disappointing, non

Notable time points

Most positive period: 2025-08-20 (mean=0.108).
Most negative period: 2025-09-17 (mean=0.055).

Top Terms

Sentiment Over Time

Predictive terms (predicting negative sentiment)

The table below lists features (tokens) that are most predictive of the negative label based on a simple logistic model.
term coef
terrible 4.092615
overbooked 4.024035
disappointing 3.860367
lost 2.614753
non 2.495278
cancelled 2.269184
delayed 2.241576
refundable 1.368705
ticket 1.019783
oked 0.870235

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Sentiment spikes

Detected spikes in rolling sentiment (z-score threshold). Review the rows to understand dates and severity.
timestamp sentiment_mean count rolling_mean rolling_std zscore spike
2025-07-09 0.075035 582 0.075035 0.000000 0.000000 False
2025-07-16 0.071834 616 0.073434 0.002263 -0.707106 False
2025-07-23 0.095979 627 0.080949 0.013114 1.146071 False
2025-07-30 0.078214 622 0.082009 0.012512 -0.303289 False
2025-08-06 0.092974 626 0.089056 0.009508 0.412125 False
2025-08-13 0.070676 614 0.080621 0.011343 -0.876852 False
2025-08-20 0.108087 622 0.090579 0.018821 0.930274 False
2025-08-27 0.085170 625 0.087978 0.018863 -0.148861 False
2025-09-03 0.081879 623 0.091712 0.014277 -0.688753 False
2025-09-10 0.085800 631 0.084283 0.002106 0.720507 False

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Topic — sentiment correlation

Correlation between LDA topic weights and VADER sentiment (positive values indicate topics associated with more positive sentiment).
topic keywords corr_with_sentiment
topic_0 experience, airport, assistance, need, voucher, delayed -0.285531
topic_1 need, grand, hyatt, flight, refund, voucher 0.298171
topic_2 staff, helpful, process, reservation, destination, non 0.355382
topic_3 seat, check, scenic, terrible, disappointing, convenient -0.000130
topic_4 paris, want, confirm, booking, voucher, luggage -0.114817
topic_5 travel, airline, offered, hotel, luggage, refundable -0.199268

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Clusters — top terms & samples

Clusters are produced by LSA embeddings + KMeans; top terms summarize each cluster and sample snippets give context.

Cluster top terms

Cluster 0: experience, dubai, sydney, rome, london, tokyo, york, new, cancelled, delayed

Cluster 1: assistance, airport, need, delayed, booked, cancelled, lost, rescheduled, enjoyed, confirmed

Cluster 2: want, confirm, paris, luggage, seat, voucher, hotel, destination, check, itinerary

Cluster 3: grand, hyatt, need, flight, hotel, refund, seat, reservation, booking, voucher

Cluster 4: airline, travel, offered, luggage, booking, hotel, destination, check, refund, itinerary

Cluster 5: staff, helpful, process, disappointing, terrible, refundable, non, overbooked, convenient, wonderful

Cluster samples

Cluster 0:
hotel paris lost experience scenic
flight new york upgraded experience first-class
hotel tokyo booked experience non-refundable

Cluster 1:
flight rescheduled airport need assistance
refund enjoyed airport need assistance
refund upgraded umm airport need assistance

Cluster 2:
want know confirm luggage terrible luggage paris
want confirm itinerary disappointing itinerary paris ticket:827967
want confirm hotel first-class hotel paris

Cluster 3:
wonderful check-in grand hyatt need get check-in
non-refundable flight grand hyatt need get flight
wonderful voucher grand hyatt need get voucher

Cluster 4:
airline upgraded itinerary offered convenient travel itinerary
airline cancelled luggage offered overbooked travel luggage ticket:452203
airline delayed seat offered know terrible travel seat

Cluster 5:
hotel process convenient staff helpful
seat process non-refundable staff helpful
seat process terrible staff helpful

Download cluster top terms • Download cluster samples